In the field of Data Analytics, the two most used programming languages ​​are R and Python. Discover which of these two languages ​​is best to learn to embark on this vocation.

In a growing number of companies, data analytics take center stage. The large amount of data available, the increasing computing power, and the effectiveness of decisions based on data analysis have given a new impetus to data science. According to IBM, in 2015, there were 2.35 million job vacancies in the field of data analysis in the United States. By 2020, this number could reach 2.72 million .

Data Analytics: R and Pyhon allow to go beyond the limits of programs like SAS and Excel

Data Analytics

Most data analysts use spreadsheet programs like Microsoft Excel or Google Sheets. Others use proprietary statistical software like SAS, Stata, or SPSS . However, these different tools also have limitations. Excel can not support datasets beyond a certain limit, and can not reproduce analyzes on new datasets. The main weakness of programs like SAS is that they have been developed for a very specific purpose, and do not benefit from a large community of contributors able to add new tools.

To overcome the limits of these tools, the only solution is to learn a programming language like R or Python. These are the two main programming languages ​​used by data analysts and data scientists . Both languages ​​are free and open source, and were developed in the early 90s. R is dedicated to statistical analysis, and Python is a more general programming language.

Data Analytics: What is the best language to learn between R and Python?

R Vs Python

These two languages ​​are ideal for working on large datasets or creating complex data visualizations, but what is the best of these programming languages ​​to learn for data analysis? Concretely, Python is better suited for data manipulation and repetitive tasks, but R is better for analyzing and exploring datasets . Indeed, unlike Python, R does not create a website and automate processes. R, on the other hand, is more suitable for heavy statistical projects and point-by-point searches of datasets.

In terms of ease of learning, the learning curve of R is steeper, and most beginners will quickly feel clueless . Python is often considered easier to learn. Another advantage of Python is that it is a more general language, which can also be used for the creation of a website or other computer program. In fact, for someone who wants to become a programmer, Python is better suited.

Anyway, in the field of data analysis, the differences between R and Python are increasingly thin. Most of the tasks that were previously associated with one or the other of these languages ​​can now be performed with these two languages. In fact, if your colleagues master one of these two languages, it may be wise to choose the same one. In conclusion, if you only want to practice data analysis, any of these two languages ​​will do the trick .